XC Analysis 2019

Naming scheme

wg1.0.e == “western gesture 1, condition 0, eastern viewer culture” eg4.2.w == “eastern gesture 4 condition 2, western viewer culture”

Part One: Overlapping Metaphors

Load the data

Western Participants

Load this straight from AMT template.

Load this from psytoolkit template.

Eastern Participants

Individual resposne graphs.

Checking correlations for question pooling to form response domains.

some things should correlate, others should not. IE the questions that go together should correlate. accessible <–> open confident <–> sure conflict <–> tension dominant <–> control goal <–> worktogether many <–> members

So now we group the metaphor measures by group and facet wrap by that.

Correlation Matrix of questions

Get wrapped plot of correlations across conditions for same gesture

Violin plots of responses to questions

Density plot

Putting it all together.

Now we can access all of the cultural-independent responses through the variables from above: all_dat$overlays[["d0_overlay"]]: the overlayed violin plot of related questions. Illustrates density overlay aka a nice vis of correlation of questions all_dat$correlation_matrix: the correlation matrices that visualize the above as well. all_dat$violin_density_question: the violin plot of all question distributions across gesture conditions. all_dat$violin_density_grouped_overlay: the violin plot of all group distributions across gesture conditions, but overlayed. all_dat$violin_density_grouped: the violin plot of group distributions across gesture conditions. all_dat$density_grouped: density plot of group distributions (density of responses for likert category) across gesture conditions. all_dat$density_question: density plot of all question distributions (density of responses for likert category) across gesture conditions.

if you want to plot all of the overlays nicely you can do this:

Build and review the group correlations.

We are correlating how well the questions map to one another in order to determine whether it is appropriate to pool the responses into the response domains through which we can analyze the cross-cultural results.

And the result is a correlation matrix of the questions for the different groups of participants. Western Gestures, Western/Eastern Participants

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Eastern Gestures, Western/Eastern Participants

To get the individual ones (per gesture, say) you can do this to position the w/e side by side.

And finally, see all of them together with

Stats

In order to determine whether any of these differences are significant (aka, did people interpret different things from each of the different gesture conditions, which, because we, too, are people, we know they did) we need to see what the significant differences between rankings in each gesture and condition are.

Cleaning and discerning differences using t and mann-whitney.

ANOVAs not necessarily appropriate because it’s non-continuous, ordered categorical data.

T-tests for mean differences in averages.

## Warning in wilcox.test.default(x = c(3L, 4L, 4L, 4L, 5L, 7L, 6L, 1L, 7L, :
## cannot compute exact p-value with ties

## Warning in wilcox.test.default(x = c(3L, 4L, 4L, 4L, 5L, 7L, 6L, 1L, 7L, :
## cannot compute exact p-value with ties

## Warning in wilcox.test.default(x = c(5L, 3L, 5L, 4L, 4L, 4L, 5L, 4L, 4L, :
## cannot compute exact p-value with ties

## Warning in wilcox.test.default(x = c(4L, 2L, 5L, 4L, 2L, 7L, 6L, 5L, 1L, :
## cannot compute exact p-value with ties

## Warning in wilcox.test.default(x = c(4L, 2L, 5L, 4L, 2L, 7L, 6L, 5L, 1L, :
## cannot compute exact p-value with ties

## Warning in wilcox.test.default(x = c(2L, 5L, 2L, 5L, 2L, 6L, 4L, 5L, 3L, :
## cannot compute exact p-value with ties

## Warning in wilcox.test.default(x = c(3L, 3L, 4L, 3L, 4L, 7L, 4L, 7L, 4L, :
## cannot compute exact p-value with ties

## Warning in wilcox.test.default(x = c(3L, 3L, 4L, 3L, 4L, 7L, 4L, 7L, 4L, :
## cannot compute exact p-value with ties

## Warning in wilcox.test.default(x = c(3L, 4L, 5L, 3L, 4L, 4L, 6L, 5L, 4L, :
## cannot compute exact p-value with ties

## Warning in wilcox.test.default(x = c(4L, 4L, 5L, 6L, 4L, 7L, 6L, 3L, 4L, :
## cannot compute exact p-value with ties

## Warning in wilcox.test.default(x = c(4L, 4L, 5L, 6L, 4L, 7L, 6L, 3L, 4L, :
## cannot compute exact p-value with ties

## Warning in wilcox.test.default(x = c(5L, 5L, 4L, 4L, 5L, 2L, 6L, 5L, 5L, :
## cannot compute exact p-value with ties

## Warning in wilcox.test.default(x = c(3L, 6L, 5L, 3L, 4L, 7L, 7L, 3L, 7L, :
## cannot compute exact p-value with ties

## Warning in wilcox.test.default(x = c(3L, 6L, 5L, 3L, 4L, 7L, 7L, 3L, 7L, :
## cannot compute exact p-value with ties

## Warning in wilcox.test.default(x = c(6L, 4L, 5L, 5L, 6L, 3L, 4L, 4L, 2L, :
## cannot compute exact p-value with ties

## Warning in wilcox.test.default(x = c(4L, 6L, 4L, 7L, 4L, 7L, 6L, 6L, 7L, :
## cannot compute exact p-value with ties

## Warning in wilcox.test.default(x = c(4L, 6L, 4L, 7L, 4L, 7L, 6L, 6L, 7L, :
## cannot compute exact p-value with ties

## Warning in wilcox.test.default(x = c(4L, 4L, 5L, 4L, 6L, 3L, 6L, 5L, 6L, :
## cannot compute exact p-value with ties

group	cond1	cond2	p	sig
conflict	cond2	cond1	0.00000	***
conflict	original	cond2	0.00000	***
unity	cond2	cond1	0.00004	***
unity	original	cond2	0.00329	**

group	cond1	cond2	p	sig
openness	cond2	cond1	0.02861	*
openness	original	cond2	0.01653	*
conflict	cond2	cond1	0.00000	***
conflict	original	cond2	0.00000	***
unity	cond2	cond1	0.00002	***
unity	original	cond2	0.00025	***

group	cond1	cond2	p	sig
conflict	cond1	original	0.00018	***

group	cond1	cond2	p	sig
conflict	cond1	original	0.00007	***
conflict	cond2	cond1	0.04688	*
size	cond1	original	0.08259	*

group	cond1	cond2	p	sig
openness	cond2	cond1	0.07005	*

group	cond1	cond2	p	sig
openness	cond2	cond1	0.00376	**
openness	original	cond2	0.00756	*
control	cond2	cond1	0.01136	*
size	cond2	cond1	0.02093	*

group	cond1	cond2	p	sig

group	cond1	cond2	p	sig

group	cond1	cond2	p	sig

group	cond1	cond2	p	sig
conflict	original	cond2	0.06143	*
unity	cond2	cond1	0.00965	*
The above s	hows that t	tests a	nd mann-wh	itney-wilcox have the same significance results.

Individual Response Patterns

Cool so we have all this but what do individuals actually perceive – multiple interpretations, or one predominant one?

Looking at individual response styles through pie chart.

– highly is 5,6,7 for likert - people who ranked X & Y highly - people who ranked X highly (but not Y) - people who ranked Y highly (but not X) - people who did neither

For example, we can visualize the difference between how individuals in western culture rated different response domains comapred to eastern cultures as below: #### Comparing cultures Notice how the amount rating “both” and “neither” changes significantly between the cultures. This indicates that the different cultures pick up different semantic cues from the gesture.

There is not a pattern in the responses in terms of one population rating “both” more often than the other. That is to say, neither culture is “more predisposed” to have multiple interpretations.

Compare interpretations between cultures

Reorganize data to have viewer_culture

Violin plot comparisons

Can only plot by one thing at a time (i.e. for a single gesture condition then visualize across metaphor measures, or for a single metaphor measure then visualize across conditions)

Now the plots live in things like wg1_violin_culture_comparisons$c0_violin_comparison

These visualizations are not very useful though, because they don’t do a good job of emphasizing the differences between cultures.

Stats

Now all that data lives in things like wg1_total_cultural_comparison_tables$c0_comparison that look like this: WG1-0 (this name isn’t here in the actual version, we just know from the naming scheme)

WG1

Original, Cond1, Cond2 Basically, “were there significant differences between western and eastern viewers in this set of conditions?”

As we said before, this visualization does a pretty lousy job of actually highlighting the differences between the cultures.

Experimental power

Short Answer: yes, power is high enough

Example usage:

cond_power <- calculate_power_for_condition(wg2_total, "conflict", "original")

Then you get a variable called cond_power you can use to see the actual power through cond_power$power. In this case our power is 0.9946474

Analyzing with high/med/low

We’re bucketing our results into high/medium/low to analyze the differences in interpretation, because either a high or low response essentially means the individual had an opinion or an interpretation of the gesture, whereas medium responses suggested no interpretation.

And now actually analyze the stats we’ve gathered

1. Tell if proportion between groups are sig.

And here are all of the results of the cultural comparison by buckets.

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condition	group	high_comp	adjusted_high_comp	med_comp	adjusted_med_comp	low_comp	adjusted_low_comp	w_high	e_high	w_med	e_med	w_low	e_low
cond1	certainty	0 ***	***	0.058		1e-04 ***	***	43	21	14	32	3	27
cond1	conflict	0.0346 *		1		0.0298 *		15	35	9	13	36	32
cond1	control	6e-04 **	**	0.0057 *	*	0.5346		33	20	12	35	15	25
cond1	unity	1e-04 ***	***	0.0035 **	*	0.1194		47	36	4	22	9	22
cond1	openness	0.0083 *	*	0.0865		0.3908		27	18	9	23	24	39
cond1	size	0.5599		0.6175		0.2445		24	27	15	16	21	37
cond2	certainty	0.0479 *		0.4545		0.1722		43	32	16	22	13	22
cond2	conflict	0.0085 *	*	0.0139 *	*	0.4186		61	49	3	14	8	13
cond2	control	0.0033 **	*	0.1334		0.1164		41	24	14	24	17	28
cond2	unity	0.0923		0.073		9e-04 **	**	27	40	8	18	37	18
cond2	openness	0.7084		0.1722		0.4334		10	8	13	22	49	46
cond2	size	0.6396		0.0324 *		0.1971		35	33	11	24	26	19
original	certainty	0.0649		0.1297		0.5753		50	27	10	14	16	15
original	conflict	0.2814		0.4337		0.7235		31	29	13	6	32	21
original	control	0.1572		0.4425		0.0325 *		32	16	16	8	28	32
original	unity	0.1984		0.5472		0.4563		49	29	12	12	15	15
original	openness	0.1181		0.0998		1		19	22	26	11	31	23
original	size	1		0.3055		0.3962		35	26	19	9	22	21

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condition	group	high_comp	adjusted_high_comp	med_comp	adjusted_med_comp	low_comp	adjusted_low_comp	w_high	e_high	w_med	e_med	w_low	e_low
cond1	certainty	0.0911		0.0378 *		1		56	33	9	16	19	15
cond1	conflict	0.0028 **	*	0.7162		2e-04 ***	**	40	47	9	9	35	8
cond1	control	0.0022 **	*	0.7232		2e-04 ***	**	30	40	14	13	40	11
cond1	unity	2e-04 ***	**	1e-04 ***	***	0.7752		52	19	10	26	22	19
cond1	openness	1e-04 ***	***	0.901		0.0022 **	*	31	5	23	19	30	40
cond1	size	0.6072		0.7532		0.9821		24	15	18	16	42	33
cond2	certainty	0.0114 *	*	0.0824		0.2919		46	34	12	24	12	20
cond2	conflict	0.3227		0.0272 *		0.6936		46	44	4	15	20	19
cond2	control	0.1787		0.4891		0.4864		41	36	12	18	17	24
cond2	unity	0.0614		0.0638		0.8397		44	36	10	22	16	20
cond2	openness	0.0018 **	*	0.3322		0.1084		24	9	14	22	32	47
cond2	size	0.1245		0.9083		0.2102		32	25	12	15	26	38
original	certainty	0.8555		1		0.8744		37	26	17	11	22	13
original	conflict	3e-04 ***	**	0.9345		0 ***	***	56	20	15	11	5	19
original	control	6e-04 **	**	0.4952		0.005 **	*	43	12	16	14	17	24
original	unity	0.8555		0.1763		0.079		37	26	15	16	24	8
original	openness	0.5125		0.5125		0.1726		19	16	19	16	38	18
original	size	0.0631		0.1377		0.6245		43	19	13	15	20	16

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condition	group	high_comp	adjusted_high_comp	med_comp	adjusted_med_comp	low_comp	adjusted_low_comp	w_high	e_high	w_med	e_med	w_low	e_low
cond1	certainty	0.1589		0.4753		0.4942		40	26	19	21	17	19
cond1	conflict	0.56		0.2933		0.9336		30	22	11	15	35	29
cond1	control	0 ***	***	0.0142 *	*	0.0173 *		52	21	6	16	18	29
cond1	unity	0.0539		0.0973		0.67		49	31	13	20	14	15
cond1	openness	0.3426		0.8805		0.2483		25	16	18	14	33	36
cond1	size	0.2576		0.5502		0.0657		44	31	13	8	19	27
cond2	certainty	0.002 **	*	0.1306		0.0381 *		60	23	22	22	10	15
cond2	conflict	0.2784		0.386		0.0687		22	20	15	14	55	26
cond2	control	0.2462		0.6029		0.5826		39	19	17	14	36	27
cond2	unity	6e-04 **	**	0.0052 *	*	0.2376		73	31	12	20	7	9
cond2	openness	7e-04 **	**	0.0084 *	*	0.2899		53	17	14	21	25	22
cond2	size	0.9553		0.4238		0.5765		63	40	14	13	15	7
original	certainty	0.0781		0.3108		0.4022		43	22	20	20	11	12
original	conflict	0.6794		0.4261		0.2015		17	15	20	19	37	20
original	control	0.0127 *	*	0.0714		0.4055		38	15	11	16	25	23
original	unity	0.6794		0.5632		1		57	39	11	11	6	4
original	openness	0.4476		0.6444		0.9264		25	14	22	19	27	21
original	size	1		0.2268		0.3044		52	38	6	9	16	7

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condition	group	high_comp	adjusted_high_comp	med_comp	adjusted_med_comp	low_comp	adjusted_low_comp	w_high	e_high	w_med	e_med	w_low	e_low
cond1	certainty	1		0.4388		0.2649		28	51	12	16	4	15
cond1	conflict	1		0.313		0.4646		14	25	8	23	22	34
cond1	control	0.2899		0.2459		0.031 *		16	21	14	17	14	44
cond1	unity	0.6181		1		0.4089		28	47	13	24	3	11
cond1	openness	0.2317		0.8889		0.0872		25	36	12	20	7	26
cond1	size	0.0037 **	*	0.9548		3e-04 ***	**	33	38	9	15	2	29
cond2	certainty	0.8704		1		0.8924		25	32	18	25	11	17
cond2	conflict	0.0891		0.1923		0.606		18	37	13	10	23	27
cond2	control	0.0275 *		0.4862		0.0034 **	*	24	18	17	18	13	38
cond2	unity	0.5348		0.9617		0.6005		32	49	12	15	10	10
cond2	openness	0.3335		0.8815		0.1925		19	19	22	28	13	27
cond2	size	0.4612		0.9246		0.5699		35	42	9	14	10	18
original	certainty	0.8583		0.1597		0.259		39	48	10	22	13	10
original	conflict	0.9317		0.1292		0.3555		16	19	7	18	39	43
original	control	0.1413		1		0.1618		20	16	13	16	29	48
original	unity	0.0728		0.0803		0.7948		51	54	5	16	6	10
original	openness	0.7672		0.7159		0.3334		34	47	13	20	15	13
original	size	0.0768		0.804		0.0724		44	44	12	18	6	18

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condition	group	high_comp	adjusted_high_comp	med_comp	adjusted_med_comp	low_comp	adjusted_low_comp	w_high	e_high	w_med	e_med	w_low	e_low
cond1	certainty	1e-04 ***	***	0.0891		0.0396 *		24	20	8	31	6	29
cond1	conflict	0.0824		0.652		0.3354		14	16	6	17	18	47
cond1	control	0.411		0.0242 *		0.0047 **	*	13	20	14	13	11	47
cond1	unity	0.0026 **	*	1		0.0011 **	**	32	43	5	12	1	25
cond1	openness	0.0157 *	*	0.8106		0.0018 **	*	23	28	12	22	3	30
cond1	size	0.2775		0.1637		0.0079 *	*	17	26	15	20	6	34
cond2	certainty	0.8053		0.6243		0.2652		23	28	18	20	7	16
cond2	conflict	0.0204 *		0.0259 *		0.8474		23	16	5	19	20	29
cond2	control	0.6043		0.7941		0.3568		15	16	17	20	16	28
cond2	unity	0.6374		0.8659		0.8659		30	36	9	14	9	14
cond2	openness	0.9781		0.8811		0.6857		22	28	15	18	11	18
cond2	size	0.0459 *		0.7978		0.0424 *		28	24	16	24	4	16
original	certainty	0.0285 *		0.2263		0.2906		27	33	8	25	9	26
original	conflict	0.2724		0.4612		0.0944		7	22	4	13	33	49
original	control	1		0.3986		0.5591		10	20	12	16	22	48
original	unity	0.3698		0.5061		0.8476		31	51	7	19	6	14
original	openness	0.3576		0.0235 *		0.4646		26	41	3	21	15	22
original	size	0.3777		0.0339 *		0.0017 **	**	20	30	18	18	6	36

## Saving 7 x 5 in image

condition	group	high_comp	adjusted_high_comp	med_comp	adjusted_med_comp	low_comp	adjusted_low_comp	w_high	e_high	w_med	e_med	w_low	e_low
cond1	certainty	1		0.9697		0.8838		36	25	17	11	15	12
cond1	conflict	0.0838		0.8488		0.1177		42	21	12	10	14	17
cond1	control	0.3007		0.6323		0.0535		39	22	18	10	11	16
cond1	unity	0.9318		0.8838		1		40	27	15	12	13	9
cond1	openness	0.1537		0.5534		0.6821		17	6	18	16	33	26
cond1	size	0.0224 *		0.3459		0.1839		34	13	20	19	14	16
cond2	certainty	0.6006		0.8576		0.3768		36	21	33	18	39	15
cond2	conflict	0.7811		0.8301		0.5149		54	25	21	9	33	20
cond2	control	0.9518		0.0242 *		0.0835		32	17	33	7	43	30
cond2	unity	0.7769		1		0.6432		66	31	19	9	23	14
cond2	openness	0.453		0.4344		0.086		42	17	36	14	30	23
cond2	size	0.541		0.5587		1		53	30	28	11	27	13
original	certainty	1		0.7932		0.6508		31	28	18	18	17	12
original	conflict	0.5622		0.3002		0.9348		26	19	10	14	30	25
original	control	0.2872		0.2872		0.0451 *		19	11	19	11	28	36
original	unity	0.3057		1		0.2651		41	42	11	9	14	7
original	openness	0.3667		0.6189		0.8167		20	23	22	16	24	19
original	size	0.9118		0.3746		0.5373		29	27	23	15	14	16

## Saving 7 x 5 in image

condition	group	high_comp	adjusted_high_comp	med_comp	adjusted_med_comp	low_comp	adjusted_low_comp	w_high	e_high	w_med	e_med	w_low	e_low
cond1	certainty	5e-04 **	**	0.0017 **	**	0.7271		53	6	25	19	16	7
cond1	conflict	1		4e-04 ***	**	0.0075 *	*	27	9	10	13	57	10
cond1	control	0.4658		0.1465		0.6905		32	8	21	12	41	12
cond1	unity	0.0652		0.0039 **	*	0.8524		71	18	8	10	15	4
cond1	openness	0.6125		0.48		1		33	9	27	12	34	11
cond1	size	6e-04 **	**	0.0279 *		0.082		64	10	18	13	12	9
cond2	certainty	0.6776		1		0.6765		43	20	27	11	32	11
cond2	conflict	0.1919		0.1226		1		60	19	16	12	26	11
cond2	control	0.0749		0.6765		0.0117 *	*	42	10	37	13	23	19
cond2	unity	1		0.2144		0.2673		51	21	18	12	33	9
cond2	openness	0.917		1		1		17	6	43	18	42	18
cond2	size	0.1994		0.6701		0.3609		55	17	31	15	16	10
original	certainty	1		1		1		49	15	20	6	23	7
original	conflict	0.248		0.8822		0.1172		53	12	13	3	26	13
original	control	0.0753		0.8204		0.1471		46	8	19	7	27	13
original	unity	1		0.3634		0.3522		55	17	14	7	23	4
original	openness	0.8032		0.6218		0.3957		22	8	20	8	50	12
original	size	0.9885		1		1		45	13	21	7	26	8

## Saving 7 x 5 in image

condition	group	high_comp	adjusted_high_comp	med_comp	adjusted_med_comp	low_comp	adjusted_low_comp	w_high	e_high	w_med	e_med	w_low	e_low
cond1	certainty	0.8133		0.7804		1		47	27	18	8	13	7
cond1	conflict	0.0922		0.9754		0.0601		38	13	9	4	31	25
cond1	control	1		0.8757		0.9694		23	12	18	11	37	19
cond1	unity	0.5737		1		0.6923		59	29	6	4	13	9
cond1	openness	0.3281		0.1143		0.7482		25	18	27	8	26	16
cond1	size	0.0812		0.6303		0.1848		46	17	16	11	16	14
cond2	certainty	0.0869		1		0.0523		51	17	20	10	13	13
cond2	conflict	0.0331 *		0.5933		0.0079 *	*	24	20	14	9	46	11
cond2	control	0.5106		0.0188 *		0.0128 *	*	34	13	17	1	33	26
cond2	unity	0.0777		0.2898		0.3277		70	27	6	6	8	7
cond2	openness	0.1619		0.6704		0.0444 *		31	9	30	12	23	19
cond2	size	0.1467		0.5703		0.3391		55	20	10	7	19	13
original	certainty	0.4254		0.9092		0.1506		55	33	18	11	9	12
original	conflict	0.4076		0.0042 **	*	0.0013 **	**	24	21	9	18	49	17
original	control	0.2202		0.1029		0.8893		33	16	19	21	30	19
original	unity	0.8419		0.4262		0.6981		62	44	14	6	6	6
original	openness	0.6097		0.561		1		25	14	20	17	37	25
original	size	0.687		0.3017		0.7101		48	30	16	16	18	10

## Saving 7 x 5 in image

condition	group	high_comp	adjusted_high_comp	med_comp	adjusted_med_comp	low_comp	adjusted_low_comp	w_high	e_high	w_med	e_med	w_low	e_low
cond1	certainty	0.0522		0.2401		0.3146		45	36	20	7	15	5
cond1	conflict	0.9622		0.5901		0.8157		30	17	17	13	33	18
cond1	control	0.7424		0.4339		0.2162		32	17	23	10	25	21
cond1	unity	0.2214		0.6773		0.3581		58	29	13	10	9	9
cond1	openness	0.6554		0.0203 *		0.0026 **	*	26	13	36	11	18	24
cond1	size	0.0303 *		1		0.0026 **	*	61	27	13	7	6	14
cond2	certainty	0.5596		0.3919		0.9831		37	22	27	10	14	8
cond2	conflict	0.4032		0.4288		1		39	16	15	11	24	13
cond2	control	0.0023 **	*	0.7753		0.0026 **	*	46	11	20	12	12	17
cond2	unity	0.0536		0.2771		0.3078		51	18	15	12	12	10
cond2	openness	0.8792		0.8708		0.6179		22	10	28	13	28	17
cond2	size	0.0357 *		0.403		0.1287		54	19	13	10	11	11
original	certainty	0.906		0.4798		0.2159		56	22	24	7	12	9
original	conflict	3e-04 ***	**	0.2464		0.0446 *		16	19	25	6	51	13
original	control	0.6191		0.2223		0.0587		35	12	31	8	26	18
original	unity	0.0101 *	*	0.8097		0.005 **	*	67	18	16	8	9	12
original	openness	0.0798		0.8688		0.1552		33	7	38	17	21	14
original	size	0.0092 *	*	0.0877		0.2114		63	16	19	14	10	8

Additional questions:

Q: How do we tell whether people HAD an interpretation, or if it was uniform confusion? A: Look instead of “low,” and “high,” the union of the two – those who had interpretation.

The way to interpret this graph is the blues are the medium values, the pinks are the extreme (high+ ) values. The light is eastern, the dark is western. The blue being higher than the pinks indicate that there was NOT a strong interpretation – that is, more people ranked it 4 less than EITHER 1,2,3 or 5,6,7 – that is to say, they chose an interpretation! This is particularly clear in the case of the conflict condition for westerners in WG1 – note that there is a very strong clear interpretation for westerners, but not at all for easterners.

You can calculate and view all of them below:

X-Culture Scale Corrections

So for all of this, we are definitely using parceling, which is the process of combining individual scores to produce a new score (we combine two survey questions to form the category groups we analyze).

Ipsatization is the process of taking the mean of individual’s response for every question, and subtracting it from all of their responses, then using that dataset as that individual’s response. This doesn’t necessarily work for us because we specifically observe “extreme” responses – our Western participants rated things both higher and lower than eastern, whereas eastern stayed towards the mean. This means that this will probably not affect our results so much… but might be worth a try anyways?

Otherwise, we could try GRS construction and correction.

First let’s measure the extent to which we’re experiencing ARS/ERS.

Interestingly, we find a narrow range of responses for both western and eastern viewers (stdev=1.69 and 1.54 respectively). Both have significantly higher proportation of mid-scale responses (W=21.4%, E=25.3%) than expected.

However this still leaves open the possibility of within-subject correction… but that still doesn’t necessarily capture what we want to know. We’re specifically testing for cross-cultural differences, so correcting based on the mean doesn’t work, and stdevs aren’t sig diff. Therefore, it’s reasonable to assume the differences we observe are due to true differences in the cultural interpretations of these gestures.

More Questions

Q: Are westerners more likely overall to interpret conflict?

A: No. Actually, western avg for conflict = 3.94, whereas eastern = 3.97.

A continuation of that question… #### Q: In what categories did the differences occur?

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group	sig_high_diffs	sig_med_diffs	sig_low_diffs	total
certainty	5	1	1	7
conflict	4	3	5	12
control	7	2	7	16
openness	5	1	3	9
size	3	0	4	7
unity	5	4	3	12
A: So there	is no particular	category in whic	h one culture wa	s more apt to rate higher than the other.

Q: Did one cultural gesture result in more differences than the other?

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total differences in W gestures: 44 total differences in E gestures: 19 A: Yes, the western gestures, but there’s no objective “correct” interpretation so who cares.

Q: But who was higher?

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##   [1]   0   2   4   6   8  10  12  14  16  18  20  22  24  26  28  30  32
##  [18]  34  36  38  40  42  44  46  48  50  52  54  56  58  60  62  64  66
##  [35]  68  70  72  74  76  78  80  82  84  86  88  90  92  94  96  98 100
##  [52] 102 104 106 108 110 112 114 116 118 120 122 124 126 128 130 132 134
##  [69] 136 138 140 142 144 146 148 150 152 154 156 158 160 162 164 166 168
##  [86] 170 172 174 176 178 180 182 184 186 188 190 192 194 196 198 200 202
## [103] 204 206 208 210 212 214 216 218 220 222 224 226 228 230 232 234 236
## [120] 238 240 242 244 246 248 250 252 254 256 258 260 262 264 266 268 270
## [137] 272 274 276 278 280 282 284 286 288 290 292 294 296 298 300 302 304
## [154] 306 308 310 312 314 316 318 320 322 324

Q: What did our score distributions actually look like?

A: Like this, aka not too different:

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Q: Was there a significant difference from uniform distribution in terms of differences between groups?

## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  tdrw
## D = 0.42857, p-value = 0.1106
## alternative hypothesis: two-sided

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A: Yes, the overal general distribution of scores is basically the same… Furthermore, the difference should be a uniform distribution… and it is.

Q: What if we break down the score distribution by gesture?

I see……. it could be the case that although overall averages are the same, the gestures in which eastern folks had interpretations were fewer?

It’s a real pain in the keister to look at each individual distribution, and frankly I don’t think that’ll help us much. What I’m more interested in is the overall pattern.

Within-culture differences

Q: did our manipulations result in sig diffs within cultures?

A: Yes, the different gesture conditions led to differences within-culture that were often in the same direction, but sometimes different. Compare them like this:

A2: so, our manipulations were generally effective, especially in the direction we expected for western culture, but sometimes for eastern as well. However, it makes sense that they won’t always be the same because the overall picture is showing us that the atomic motion features of a gesture are interpreted differently by different cultures.

Q: how did our manipulations influence each culture?

build the table to look like: viewer_culture | original.mean | group | condition1 | condition 2 | p.diff.1 | p.diff.2 w 4.92 cert 5.17 4.62 0.284 0.259 e 4.27 cert 3.775 4.18 0.05 0.739

And also check directionality of differences

This is quite interesting! It shows that our manipulations only occasionally caused significant differences from the original gesture, and sometimes even that was in opposite directions!!

Let’s look at a few of them side-by-side

viewer_culture	original.mean	group	condition.1.mean	condition.2.mean	p.1	p.2	p1.sig.diff	p1.diff	p2.sig.diff	p2.diff	gesture_name
western	4.921	certainty	5.167	4.625	0.284	0.050		^	*	v	wg1
eastern	4.268	certainty	3.775	4.184	0.259	0.739		v		v	wg1
western	3.855	conflict	3.133	5.889	0.030	0.440	*	v		^	wg1
eastern	4.161	conflict	3.938	4.987	0.000	0.004	*	v	*	^	wg1
western	4.211	control	4.417	4.694	0.482	0.531		^		^	wg1
eastern	3.643	control	3.812	3.934	0.082	0.276		^		^	wg1
western	3.711	openness	3.900	2.986	0.500	0.017		^	*	v	wg1
eastern	4.107	openness	3.500	3.105	0.004	0.000	*	v	*	v	wg1
western	4.276	size	4.217	4.292	0.841	0.224		v		^	wg1
eastern	4.179	size	3.837	4.197	0.958	0.941		v		^	wg1
western	5.105	unity	5.400	3.625	0.295	0.356		^		v	wg1
eastern	4.536	unity	4.275	4.355	0.000	0.529	*	v		v	wg1

viewer_culture	original.mean	group	condition.1.mean	condition.2.mean	p.1	p.2	p1.sig.diff	p1.diff	p2.sig.diff	p2.diff	gesture_name
western	4.276	certainty	4.798	4.957	0.036	0.748	*	^		^	wg2
eastern	4.540	certainty	4.453	4.308	0.006	0.377	*	v		v	wg2
western	5.487	conflict	4.012	4.957	0.000	0.001	*	v	*	v	wg2
eastern	3.940	conflict	4.922	4.474	0.063	0.078		^		^	wg2
western	4.618	control	3.893	4.600	0.005	0.000	*	v	*	v	wg2
eastern	3.480	control	4.703	4.077	0.943	0.052		^		^	wg2
western	3.382	openness	4.012	3.657	0.014	0.001	*	^	*	^	wg2
eastern	3.780	openness	2.938	3.051	0.309	0.005		v	*	v	wg2
western	4.421	size	3.702	4.171	0.008	0.013	*	v	*	v	wg2
eastern	4.120	size	3.406	3.564	0.374	0.042		v	*	v	wg2
western	4.263	unity	4.702	4.929	0.076	0.034		^	*	^	wg2
eastern	4.540	unity	3.984	4.321	0.014	0.392	*	v		v	wg2

viewer_culture	original.mean	group	condition.1.mean	condition.2.mean	p.1	p.2	p1.sig.diff	p1.diff	p2.sig.diff	p2.diff	gesture_name
western	4.784	certainty	4.500	4.989	0.267	0.801		v		^	wg3
eastern	4.259	certainty	4.197	4.233	0.309	0.910		v		v	wg3
western	3.297	conflict	3.697	3.120	0.159	0.685		^		v	wg3
eastern	3.722	conflict	3.833	3.950	0.486	0.415		^		^	wg3
western	4.324	control	4.882	3.924	0.065	0.822		^		v	wg3
eastern	3.667	control	3.727	3.683	0.162	0.953		^		^	wg3
western	3.757	openness	3.684	4.739	0.794	0.377		v		^	wg3
eastern	3.722	openness	3.500	3.800	0.000	0.755	*	v		^	wg3
western	5.027	size	4.579	5.446	0.129	0.002		v	*	^	wg3
eastern	4.963	size	4.106	4.833	0.125	0.589		v		v	wg3
western	5.446	unity	4.908	5.554	0.030	0.048	*	v	*	^	wg3
eastern	5.000	unity	4.500	4.600	0.614	0.089		v		v	wg3

viewer_culture	original.mean	group	condition.1.mean	condition.2.mean	p.1	p.2	p1.sig.diff	p1.diff	p2.sig.diff	p2.diff	gesture_name
western	4.903	certainty	4.955	4.444	0.851	0.984		^		v	wg4
eastern	4.825	certainty	4.829	4.338	0.099	0.018		^	*	v	wg4
western	3.145	conflict	3.545	3.907	0.231	0.067		^		^	wg4
eastern	3.325	conflict	3.780	4.068	0.019	0.007	*	^	*	^	wg4
western	3.726	control	4.045	4.463	0.280	0.115		^		^	wg4
eastern	3.163	control	3.537	3.351	0.012	0.451	*	^		^	wg4
western	4.645	openness	4.659	4.111	0.959	0.005		^	*	v	wg4
eastern	4.750	openness	4.159	3.784	0.050	0.000	*	v	*	v	wg4
western	5.210	size	5.432	4.778	0.393	0.081		^		v	wg4
eastern	4.600	size	4.195	4.527	0.107	0.752		v		v	wg4
western	5.306	unity	4.955	4.741	0.161	0.134		v		v	wg4
eastern	4.950	unity	4.646	4.851	0.044	0.630	*	v		v	wg4

viewer_culture	original.mean	group	condition.1.mean	condition.2.mean	p.1	p.2	p1.sig.diff	p1.diff	p2.sig.diff	p2.diff	gesture_name
western	4.364	certainty	5.079	4.479	0.051	0.107		^		^	wg5
eastern	4.179	certainty	3.812	4.266	0.712	0.705		v		^	wg5
western	2.886	conflict	3.579	4.000	0.109	0.961		^		^	wg5
eastern	3.274	conflict	3.263	3.578	0.005	0.208	*	v		^	wg5
western	3.318	control	4.158	3.917	0.027	0.474	*	^		^	wg5
eastern	3.298	control	3.487	3.656	0.078	0.158		^		^	wg5
western	4.455	openness	4.947	4.271	0.171	0.332		^		v	wg5
eastern	4.214	openness	3.962	4.391	0.579	0.476		v		^	wg5
western	4.455	size	4.684	4.708	0.438	0.638		^		^	wg5
eastern	3.857	size	3.737	4.172	0.343	0.231		v		^	wg5
western	5.159	unity	5.895	4.875	0.019	0.034	*	^	*	v	wg5
eastern	4.857	unity	4.338	4.484	0.378	0.114		v		v	wg5

viewer_culture	original.mean	group	condition.1.mean	condition.2.mean	p.1	p.2	p1.sig.diff	p1.diff	p2.sig.diff	p2.diff	gesture_name
western	4.212	certainty	4.618	3.944	0.114	0.797		^		v	eg1
eastern	4.466	certainty	4.396	4.093	0.217	0.136		v		v	eg1
western	3.894	conflict	4.647	4.157	0.010	0.318	*	^		^	eg1
eastern	3.862	conflict	4.188	4.185	0.312	0.291		^		^	eg1
western	3.636	control	4.912	3.713	0.000	0.002	*	^	*	^	eg1
eastern	3.190	control	4.229	3.278	0.752	0.782		^		^	eg1
western	3.939	openness	3.368	4.231	0.027	0.003	*	v	*	^	eg1
eastern	4.000	openness	3.167	3.685	0.176	0.294		v		v	eg1
western	4.303	size	4.529	4.380	0.335	0.055		^		^	eg1
eastern	4.276	size	3.771	4.444	0.709	0.541		v		^	eg1
western	4.773	unity	4.691	4.769	0.778	0.113		v		v	eg1
eastern	4.862	unity	4.438	4.648	0.988	0.405		v		v	eg1

viewer_culture	original.mean	group	condition.1.mean	condition.2.mean	p.1	p.2	p1.sig.diff	p1.diff	p2.sig.diff	p2.diff	gesture_name
western	4.446	certainty	4.723	4.098	0.208	0.276		^		v	eg2
eastern	4.357	certainty	4.000	4.310	0.167	0.893		v		v	eg2
western	4.446	conflict	3.255	4.686	0.000	0.992	*	v		^	eg2
eastern	3.964	conflict	3.969	4.310	0.376	0.437		^		^	eg2
western	4.391	control	3.713	4.206	0.004	0.632	*	v		v	eg2
eastern	3.607	control	3.812	3.714	0.418	0.802		^		^	eg2
western	3.370	openness	3.979	3.441	0.005	0.923	*	^		^	eg2
eastern	3.786	openness	3.750	3.452	0.741	0.312		v		v	eg2
western	4.293	size	4.894	4.667	0.005	0.582	*	^		^	eg2
eastern	4.214	size	4.000	4.095	0.096	0.759		v		v	eg2
western	4.685	unity	5.309	4.275	0.007	0.879	*	^		v	eg2
eastern	4.643	unity	4.688	4.452	0.092	0.494		^		v	eg2

viewer_culture	original.mean	group	condition.1.mean	condition.2.mean	p.1	p.2	p1.sig.diff	p1.diff	p2.sig.diff	p2.diff	gesture_name
western	4.963	certainty	4.679	4.786	0.169	0.799		v		v	eg3
eastern	4.714	certainty	4.786	4.150	0.420	0.087		^		v	eg3
western	3.317	conflict	4.000	3.179	0.018	0.047	*	^	*	v	eg3
eastern	4.054	conflict	3.476	4.425	0.622	0.236		v		^	eg3
western	4.037	control	3.795	3.905	0.343	0.173		v		v	eg3
eastern	3.946	control	3.571	3.300	0.627	0.052		v		v	eg3
western	3.707	openness	3.987	3.988	0.258	0.142		^		^	eg3
eastern	3.696	openness	4.143	3.475	0.258	0.494		^		v	eg3
western	4.707	size	4.718	4.750	0.965	0.034		^	*	^	eg3
eastern	4.571	size	4.000	4.275	0.854	0.353		v		v	eg3
western	5.476	unity	5.282	5.548	0.416	0.750		v		^	eg3
eastern	4.982	unity	4.881	4.875	0.746	0.716		v		v	eg3

viewer_culture	original.mean	group	condition.1.mean	condition.2.mean	p.1	p.2	p1.sig.diff	p1.diff	p2.sig.diff	p2.diff	gesture_name
western	4.804	certainty	4.612	4.718	0.321	0.252		v		v	eg4
eastern	4.579	certainty	4.917	4.425	0.669	0.629		^		v	eg4
western	3.337	conflict	3.900	4.333	0.010	0.473	*	^		^	eg4
eastern	4.158	conflict	3.938	4.000	0.000	0.627	*	v		v	eg4
western	4.185	control	4.162	4.731	0.921	0.506		v		^	eg4
eastern	3.605	control	3.833	3.600	0.007	0.989	*	^		v	eg4
western	4.207	openness	4.188	3.936	0.918	0.626		v		v	eg4
eastern	3.579	openness	3.729	3.600	0.170	0.948		^		^	eg4
western	5.141	size	5.088	4.987	0.776	0.732		v		v	eg4
eastern	4.316	size	4.208	4.425	0.461	0.719		v		^	eg4
western	5.261	unity	4.975	4.923	0.154	0.126		v		v	eg4
eastern	4.316	unity	4.792	4.400	0.128	0.791		^		^	eg4
A: We see that ou	r manipulations	led to chang	es in different are	as all across the b	oard.